Modal Parameter Identification of Bridge Using Data-driven Stochastic Subspace Algorithm

2011 ◽  
Author(s):  
Deshan Shan ◽  
Min Xu ◽  
Qiao Li
Keyword(s):  
Parameter Identification ◽  
Data Driven ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Subspace Algorithm ◽  
Using Data

scholarly journals Application of Improved Combined Deterministic-Stochastic Subspace Algorithm in Bridge Modal Parameter Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peng Wen ◽  
Inamullah Khan ◽  
Jie He ◽  
Qiaofeng Chen
Keyword(s):  
Parameter Identification ◽  
Clustering Algorithm ◽  
Identification Algorithm ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Fuzzy C Means ◽  
Subspace Algorithm ◽  
Bridge Structures ◽  
Fuzzy C Means Clustering ◽  
Online Tracking

Modal parameter identification is considered to be one of the most important tasks in structural health monitoring because it provides a reliable reference for structural vibration control, damage severity, and operational state. Moreover, at present, the combined deterministic-stochastic subspace algorithm is cogitated as one of the key algorithms in the modal parameter identification, which is why it is widely used in the modal parameter identification of bridge structures. In this paper, a novel method is proposed, which is a time-domain identification algorithm, based on sliding window-fuzzy C-means clustering algorithm-combined with deterministic-stochastic subspace identification (SC-CDSI), to achieve online intelligent tracking and identification of modal parameters for nonlinear time-varying structures. First of all, to realize the online tracking and identification process, it is necessary to divide the input and output signal of the nonlinear time-varying structure by windowing; for that, to determine the window function, window size and window step length according to the characteristics of the signal are analyzed. Secondly, in order to satisfy the intelligent identification of effective modals in stability diagram, the fuzzy C-means clustering algorithm is kept as a base, whereas frequency, damping ratio, and modal shapes serve as clustering elements, applied to fuzzy C-means clustering algorithm, and then the intelligent selection of effective modals is achieved. Finally, a shaking table test bridge is used as a modal parameter identification in lab, and its results are compared with the MIDAS finite element results. The compared results show that the proposed SC-CDSI identification algorithm can accurately achieve the intelligent identification of online tracking of the structural frequency, and the identification results are reliable to be used in real-life bridge structures.

Application of Modal Analysis to Cylinder Structure Based on Random Vibration Test

2006 ◽  
Vol 5-6 ◽  
pp. 225-230
Author(s):  
C. Li ◽  
S. Hu ◽  
J. Li
Keyword(s):  
Parameter Identification ◽  
Modal Analysis ◽  
Random Vibration ◽  
Impulse Response Function ◽  
Vibration Test ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Exponential Method ◽  
Using Data ◽  
Complex Exponential

This paper presents the application of modal analysis and parameter identification using data of random vibration test to a cylinder structure. It is deduced that the impulse response function can be substituted by the cross-correlation function computed directly from the response-only data based on the white noise excitation or broadband input. So the modal parameter identification can be done by the least squares complex exponential (LSCE) method. And the modal parameters are obtained from the random vibration test data by the complex exponential method. Compared with the results of traditional modal test, the results of random vibration test are proved to be correct, and also the feasibility of this idea is proved well.

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